






"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__SCREAMING_SNAKE_CASE   =   logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				Optional[str]      =  field(
					    default="""cifar10"""   ,     metadata={"""help""": """Name of a dataset from the datasets package"""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """The column name of the images in the files."""}						)
					lowercase_    :				Optional[str]      =  field(default=UpperCamelCase   ,     metadata={"""help""": """A folder containing the training data."""}						)
					lowercase_    :				Optional[str]      =  field(default=UpperCamelCase   ,     metadata={"""help""": """A folder containing the validation data."""}						)
					lowercase_    :				Optional[float]      =  field(
					    default=0.15   ,     metadata={"""help""": """Percent to split off of train for validation."""}						)
					lowercase_    :				Optional[int]      =  field(
					    default=UpperCamelCase   ,     metadata={
					        """help""": (
					            """For debugging purposes or quicker training, truncate the number of training examples to this """
					            """value if set."""
					        )
					    }   ,     )
					lowercase_    :				Optional[int]      =  field(
					    default=UpperCamelCase   ,     metadata={
					        """help""": (
					            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
					            """value if set."""
					        )
					    }   ,     )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       List[Any]:
											lowerCAmelCase   :List[str]						=			{}
											if self.train_dir is not None:
																	lowerCAmelCase   :int						=			self.train_dir
											if self.validation_dir is not None:
																	lowerCAmelCase   :Dict						=			self.validation_dir
											lowerCAmelCase   :List[str]						=			data_files if data_files else None




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				str      =  field(
					    default=UpperCamelCase   ,     metadata={
					        """help""": (
					            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
					        )
					    }   ,     )
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={
					        """help""": (
					            """Override some existing default config settings when a model is trained from scratch. Example: """
					            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
					        )
					    }   ,     )
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}						)
					lowercase_    :				str      =  field(
					    default="""main"""   ,     metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}   ,     )
					lowercase_    :				str      =  field(default=UpperCamelCase   ,     metadata={"""help""": """Name or path of preprocessor config."""}						)
					lowercase_    :				bool      =  field(
					    default=UpperCamelCase   ,     metadata={
					        """help""": (
					            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
					            """with private models)."""
					        )
					    }   ,     )
					lowercase_    :				float      =  field(
					    default=0.75   ,     metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}						)
					lowercase_    :				bool      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}						)




@dataclass
class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				float      =  field(
					    default=1e-3   ,     metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}						)


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			torch.stack([example['pixel_values'] for example in examples] )
						return {"pixel_values": pixel_values}


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :int						=			HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
						if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Any						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
						else:
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			parser.parse_args_into_dataclasses()

						# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
						# information sent is the one passed as arguments along with your Python/PyTorch versions.
						send_example_telemetry('run_mae'     ,      a__     ,      a__ )

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,      datefmt='%m/%d/%Y %H:%M:%S'     ,      handlers=[logging.StreamHandler(sys.stdout )]     ,      )

						if training_args.should_log:
												# The default of training_args.log_level is passive, so we set log level at info here to have that default.
												transformers.utils.logging.set_verbosity_info()

						lowerCAmelCase   :Optional[Any]						=			training_args.get_process_log_level()
						logger.setLevel(a__ )
						transformers.utils.logging.set_verbosity(a__ )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()

						# Log on each process the small summary:
						logger.warning(
						    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
						    + F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
						logger.info(F"""Training/evaluation parameters {training_args}""" )

						# Detecting last checkpoint.
						lowerCAmelCase   :List[Any]						=			None
						if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
												lowerCAmelCase   :str						=			get_last_checkpoint(training_args.output_dir )
												if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
																		raise ValueError(
																		    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																		    'Use --overwrite_output_dir to overcome.' )
												elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																		logger.info(
																		    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																		    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )

    # Initialize our dataset.
						lowerCAmelCase   :Optional[int]						=			load_dataset(
						    data_args.dataset_name     ,      data_args.dataset_config_name     ,      data_files=data_args.data_files     ,      cache_dir=model_args.cache_dir     ,      use_auth_token=True if model_args.use_auth_token else None     ,      )

						# If we don't have a validation split, split off a percentage of train as validation.
						lowerCAmelCase   :Any						=			None if 'validation' in ds.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split     ,      a__ ) and data_args.train_val_split > 0.0:
												lowerCAmelCase   :Any						=			ds['train'].train_test_split(data_args.train_val_split )
												lowerCAmelCase   :Optional[Any]						=			split['train']
												lowerCAmelCase   :Dict						=			split['test']

						# Load pretrained model and image processor
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						lowerCAmelCase   :Union[str, Any]						=			{
						    'cache_dir': model_args.cache_dir,
						    'revision': model_args.model_revision,
						    'use_auth_token': True if model_args.use_auth_token else None,
						}
						if model_args.config_name:
												lowerCAmelCase   :Dict						=			ViTMAEConfig.from_pretrained(model_args.config_name     ,      **a__ )
						elif model_args.model_name_or_path:
												lowerCAmelCase   :List[Any]						=			ViTMAEConfig.from_pretrained(model_args.model_name_or_path     ,      **a__ )
						else:
												lowerCAmelCase   :int						=			ViTMAEConfig()
												logger.warning('You are instantiating a new config instance from scratch.' )
												if model_args.config_overrides is not None:
																		logger.info(F"""Overriding config: {model_args.config_overrides}""" )
																		config.update_from_string(model_args.config_overrides )
																		logger.info(F"""New config: {config}""" )

    # adapt config
						config.update(
						    {
						        'mask_ratio': model_args.mask_ratio,
						        'norm_pix_loss': model_args.norm_pix_loss,
						    } )

						# create image processor
						if model_args.image_processor_name:
												lowerCAmelCase   :Union[str, Any]						=			ViTImageProcessor.from_pretrained(model_args.image_processor_name     ,      **a__ )
						elif model_args.model_name_or_path:
												lowerCAmelCase   :int						=			ViTImageProcessor.from_pretrained(model_args.model_name_or_path     ,      **a__ )
						else:
												lowerCAmelCase   :Dict						=			ViTImageProcessor()

						# create model
						if model_args.model_name_or_path:
												lowerCAmelCase   :List[Any]						=			ViTMAEForPreTraining.from_pretrained(
												    model_args.model_name_or_path     ,      from_tf=bool('.ckpt' in model_args.model_name_or_path )     ,      config=a__     ,      cache_dir=model_args.cache_dir     ,      revision=model_args.model_revision     ,      use_auth_token=True if model_args.use_auth_token else None     ,      )
						else:
												logger.info('Training new model from scratch' )
												lowerCAmelCase   :Tuple						=			ViTMAEForPreTraining(a__ )

						if training_args.do_train:
												lowerCAmelCase   :Any						=			ds['train'].column_names
						else:
												lowerCAmelCase   :Tuple						=			ds['validation'].column_names

						if data_args.image_column_name is not None:
												lowerCAmelCase   :List[Any]						=			data_args.image_column_name
						elif "image" in column_names:
												lowerCAmelCase   :Tuple						=			'image'
						elif "img" in column_names:
												lowerCAmelCase   :Dict						=			'img'
						else:
												lowerCAmelCase   :List[Any]						=			column_names[0]

						# transformations as done in original MAE paper
						# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
						if "shortest_edge" in image_processor.size:
												lowerCAmelCase   :List[Any]						=			image_processor.size['shortest_edge']
						else:
												lowerCAmelCase   :Dict						=			(image_processor.size['height'], image_processor.size['width'])
						lowerCAmelCase   :Union[str, Any]						=			Compose(
						    [
						        Lambda(lambda a__ : img.convert('RGB' ) if img.mode != "RGB" else img ),
						        RandomResizedCrop(a__     ,      scale=(0.2, 1.0)     ,      interpolation=InterpolationMode.BICUBIC ),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        Normalize(mean=image_processor.image_mean     ,      std=image_processor.image_std ),
						    ] )

						def preprocess_images(a__ ):
												lowerCAmelCase   :Union[str, Any]						=			[transforms(a__ ) for image in examples[image_column_name]]
												return examples

						if training_args.do_train:
												if "train" not in ds:
																		raise ValueError('--do_train requires a train dataset' )
												if data_args.max_train_samples is not None:
																		lowerCAmelCase   :int						=			ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
												# Set the training transforms
												ds["train"].set_transform(a__ )

						if training_args.do_eval:
												if "validation" not in ds:
																		raise ValueError('--do_eval requires a validation dataset' )
												if data_args.max_eval_samples is not None:
																		lowerCAmelCase   :Any						=			(
																		    ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
																		)
												# Set the validation transforms
												ds["validation"].set_transform(a__ )

						# Compute absolute learning rate
						lowerCAmelCase   :List[Any]						=			(
						    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
						)
						if training_args.base_learning_rate is not None:
												lowerCAmelCase   :int						=			training_args.base_learning_rate * total_train_batch_size / 2_56

						# Initialize our trainer
						lowerCAmelCase   :str						=			Trainer(
						    model=a__     ,      args=a__     ,      train_dataset=ds['train'] if training_args.do_train else None     ,      eval_dataset=ds['validation'] if training_args.do_eval else None     ,      tokenizer=a__     ,      data_collator=a__     ,      )

						# Training
						if training_args.do_train:
												lowerCAmelCase   :Any						=			None
												if training_args.resume_from_checkpoint is not None:
																		lowerCAmelCase   :Union[str, Any]						=			training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		lowerCAmelCase   :Optional[Any]						=			last_checkpoint
												lowerCAmelCase   :Union[str, Any]						=			trainer.train(resume_from_checkpoint=a__ )
												trainer.save_model()
												trainer.log_metrics('train'     ,      train_result.metrics )
												trainer.save_metrics('train'     ,      train_result.metrics )
												trainer.save_state()

						# Evaluation
						if training_args.do_eval:
												lowerCAmelCase   :List[Any]						=			trainer.evaluate()
												trainer.log_metrics('eval'     ,      a__ )
												trainer.save_metrics('eval'     ,      a__ )

						# Write model card and (optionally) push to hub
						lowerCAmelCase   :Tuple						=			{
						    'tasks': 'masked-auto-encoding',
						    'dataset': data_args.dataset_name,
						    'tags': ['masked-auto-encoding'],
						}
						if training_args.push_to_hub:
												trainer.push_to_hub(**a__ )
						else:
												trainer.create_model_card(**a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")